{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Autoencoders`\n",
    "`Autoencoders`能够从无标签数据中学习到数据的表征`(latent representations)`，这样的表征相比输入数据通常维度较低；    \n",
    " - 可用于**降维**，特别是可视化目的；\n",
    " - 也可用作**特征提取器**，用于非监督的预训练深度神经网络；\n",
    " - 此外一些特定类型的`autoencoder`也是生成模型，能够随机生成类似于输入数据的新数据，但通常效果较差\n",
    " \n",
    "`Autoencoders`通常由两部分组成：\n",
    " - 编码器`encoder`：将输入转换成潜在表征\n",
    " - 解码器`decoder`：将表征转换为输出\n",
    "     - 通常解码器的输出层的神经元个数与`autoencoder`输入的个数相同；输出也称为“重构”，尝试重新生成输入\n",
    "     - 损失函数为 重构的输出 与 原始的输入 的差别\n",
    "     - 潜在表征的维度通常比输入的维度低，称为`undercomplete`；因此，网络会被迫学习输入的重要特征，丢弃不重要的信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 线性自编码器用于`PCA`\n",
    "- **不使用激活函数，所有神经元都是线性的！！**\n",
    "- 下面的示例将三维数据降低到二维   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T08:20:01.095689Z",
     "start_time": "2020-04-27T08:20:01.088493Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60, 3)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(4)\n",
    "\n",
    "\n",
    "# 生成3维的实验数据\n",
    "def generate_3d_data(m, w1=0.1, w2=0.3, noise=0.1):\n",
    "    angles = np.random.rand(m) * 3 * np.pi / 2 - 0.5\n",
    "    data = np.empty((m, 3))\n",
    "    data[:, 0] = np.cos(\n",
    "        angles) + np.sin(angles) / 2 + noise * np.random.randn(m) / 2\n",
    "    data[:, 1] = np.sin(angles) * 0.7 + noise * np.random.randn(m) / 2\n",
    "    data[:, 2] = data[:, 0] * w1 + data[:, 1] * w2 + noise * np.random.randn(m)\n",
    "    return data\n",
    "\n",
    "\n",
    "x_train = generate_3d_data(60)\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T08:20:02.345383Z",
     "start_time": "2020-04-27T08:20:02.013640Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_15\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "sequential_13 (Sequential)   (None, 2)                 8         \n",
      "_________________________________________________________________\n",
      "sequential_14 (Sequential)   (None, 3)                 9         \n",
      "=================================================================\n",
      "Total params: 17\n",
      "Trainable params: 17\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 60 samples\n",
      "Epoch 1/20\n",
      "60/60 [==============================] - 0s 2ms/sample - loss: 0.3469\n",
      "Epoch 2/20\n",
      "60/60 [==============================] - 0s 49us/sample - loss: 0.2269\n",
      "Epoch 3/20\n",
      "60/60 [==============================] - 0s 42us/sample - loss: 0.1631\n",
      "Epoch 4/20\n",
      "60/60 [==============================] - 0s 46us/sample - loss: 0.1273\n",
      "Epoch 5/20\n",
      "60/60 [==============================] - 0s 44us/sample - loss: 0.1029\n",
      "Epoch 6/20\n",
      "60/60 [==============================] - 0s 39us/sample - loss: 0.0864\n",
      "Epoch 7/20\n",
      "60/60 [==============================] - 0s 45us/sample - loss: 0.0748\n",
      "Epoch 8/20\n",
      "60/60 [==============================] - 0s 49us/sample - loss: 0.0662\n",
      "Epoch 9/20\n",
      "60/60 [==============================] - 0s 41us/sample - loss: 0.0592\n",
      "Epoch 10/20\n",
      "60/60 [==============================] - 0s 34us/sample - loss: 0.0541\n",
      "Epoch 11/20\n",
      "60/60 [==============================] - 0s 40us/sample - loss: 0.0503\n",
      "Epoch 12/20\n",
      "60/60 [==============================] - 0s 37us/sample - loss: 0.0473\n",
      "Epoch 13/20\n",
      "60/60 [==============================] - 0s 38us/sample - loss: 0.0448\n",
      "Epoch 14/20\n",
      "60/60 [==============================] - 0s 43us/sample - loss: 0.0430\n",
      "Epoch 15/20\n",
      "60/60 [==============================] - 0s 43us/sample - loss: 0.0415\n",
      "Epoch 16/20\n",
      "60/60 [==============================] - 0s 41us/sample - loss: 0.0403\n",
      "Epoch 17/20\n",
      "60/60 [==============================] - 0s 42us/sample - loss: 0.0395\n",
      "Epoch 18/20\n",
      "60/60 [==============================] - 0s 44us/sample - loss: 0.0385\n",
      "Epoch 19/20\n",
      "60/60 [==============================] - 0s 43us/sample - loss: 0.0377\n",
      "Epoch 20/20\n",
      "60/60 [==============================] - 0s 41us/sample - loss: 0.0371\n",
      "(60, 2)\n"
     ]
    }
   ],
   "source": [
    "x_train = x_train - x_train.mean(axis=0, keepdims=0)\n",
    "\n",
    "# 编码器，输入3维，输出2维\n",
    "encoder = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Dense(2, input_shape=[3]),\n",
    "])\n",
    "\n",
    "# 解码器，输入2维，输出3维\n",
    "decoder = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Dense(3, input_shape=[2]),\n",
    "])\n",
    "\n",
    "# 创建并编译模型\n",
    "autoencoder = tf.keras.models.Sequential([encoder, decoder])\n",
    "autoencoder.compile(loss='mse', optimizer=tf.keras.optimizers.SGD(lr=0.1))\n",
    "autoencoder.summary()\n",
    "\n",
    "# 训练模型\n",
    "history = autoencoder.fit(x_train, x_train, epochs=20, verbose=1)\n",
    "\n",
    "# 获得编码\n",
    "codings = encoder.predict(x_train)\n",
    "print(codings.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T08:20:05.762670Z",
     "start_time": "2020-04-27T08:20:05.595558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4, 3))\n",
    "plt.plot(codings[:, 0], codings[:, 1], \"b.\")\n",
    "plt.xlabel(\"$z_1$\", fontsize=18)\n",
    "plt.ylabel(\"$z_2$\", fontsize=18, rotation=0)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 堆叠自编码器\n",
    "- 下面的示例将重构 `MNIST` 数据\n",
    "- 损失函数不再是 `MSE`，而是二元交叉熵；看作**二分类问题**，输出每一个像素是黑色还是白色，可以加快训练       \n",
    "   \n",
    "<img src=\"../images/Stacked_autoencoder.jpg\" width=\"50%\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T09:21:45.966822Z",
     "start_time": "2020-04-27T09:21:45.696154Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5000, 784), (5000,))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 手动加载数据\n",
    "def load_mnist(path, kind='train'):\n",
    "    import os\n",
    "    import gzip\n",
    "\n",
    "    labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind)\n",
    "    images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind)\n",
    "    with gzip.open(labels_path, 'rb') as lbpath:\n",
    "        labels = np.frombuffer(\n",
    "            lbpath.read(),\n",
    "            dtype=np.uint8,\n",
    "            offset=8,\n",
    "        )\n",
    "    with gzip.open(images_path, 'rb') as imgpath:\n",
    "        images = np.frombuffer(\n",
    "            imgpath.read(),\n",
    "            dtype=np.uint8,\n",
    "            offset=16,\n",
    "        ).reshape(len(labels), 784)\n",
    "    return images, labels\n",
    "\n",
    "\n",
    "path = '../datasets/fashion-mnist/'\n",
    "x_train, y_train = load_mnist(path, kind='train')\n",
    "x_test, y_test = load_mnist(path, kind='t10k')\n",
    "\n",
    "x_train = x_train.astype(np.float32) / 255\n",
    "x_train, x_val = x_train[:5000], x_train[:5000]\n",
    "y_train, y_val = y_train[:5000], y_train[:5000]\n",
    "\n",
    "# 数据格式\n",
    "x_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T09:17:50.010464Z",
     "start_time": "2020-04-27T09:17:50.005386Z"
    }
   },
   "outputs": [],
   "source": [
    "def round_accuracy(y_true, y_pred):\n",
    "    return tf.keras.metrics.binary_accuracy(tf.round(y_true), tf.round(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T09:21:03.960510Z",
     "start_time": "2020-04-27T09:20:56.619865Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 5000 samples, validate on 5000 samples\n",
      "Epoch 1/20\n",
      "5000/5000 [==============================] - 1s 123us/sample - loss: 0.4759 - val_loss: 0.3952\n",
      "Epoch 2/20\n",
      "5000/5000 [==============================] - 0s 71us/sample - loss: 0.3734 - val_loss: 0.3848\n",
      "Epoch 3/20\n",
      "5000/5000 [==============================] - 0s 68us/sample - loss: 0.3531 - val_loss: 0.3464\n",
      "Epoch 4/20\n",
      "5000/5000 [==============================] - 0s 67us/sample - loss: 0.3357 - val_loss: 0.3309\n",
      "Epoch 5/20\n",
      "5000/5000 [==============================] - 0s 68us/sample - loss: 0.3268 - val_loss: 0.3221\n",
      "Epoch 6/20\n",
      "5000/5000 [==============================] - 0s 69us/sample - loss: 0.3212 - val_loss: 0.3216\n",
      "Epoch 7/20\n",
      "5000/5000 [==============================] - 0s 70us/sample - loss: 0.3189 - val_loss: 0.3181\n",
      "Epoch 8/20\n",
      "5000/5000 [==============================] - 0s 68us/sample - loss: 0.3163 - val_loss: 0.3157\n",
      "Epoch 9/20\n",
      "5000/5000 [==============================] - 0s 70us/sample - loss: 0.3165 - val_loss: 0.3162\n",
      "Epoch 10/20\n",
      "5000/5000 [==============================] - 0s 74us/sample - loss: 0.3134 - val_loss: 0.3142\n",
      "Epoch 11/20\n",
      "5000/5000 [==============================] - 0s 71us/sample - loss: 0.3127 - val_loss: 0.3130\n",
      "Epoch 12/20\n",
      "5000/5000 [==============================] - 0s 66us/sample - loss: 0.3107 - val_loss: 0.3118\n",
      "Epoch 13/20\n",
      "5000/5000 [==============================] - 0s 72us/sample - loss: 0.3101 - val_loss: 0.3164\n",
      "Epoch 14/20\n",
      "5000/5000 [==============================] - 0s 69us/sample - loss: 0.3084 - val_loss: 0.3081\n",
      "Epoch 15/20\n",
      "5000/5000 [==============================] - 0s 66us/sample - loss: 0.3071 - val_loss: 0.3072\n",
      "Epoch 16/20\n",
      "5000/5000 [==============================] - 0s 68us/sample - loss: 0.3072 - val_loss: 0.3073\n",
      "Epoch 17/20\n",
      "5000/5000 [==============================] - 0s 69us/sample - loss: 0.3059 - val_loss: 0.3049\n",
      "Epoch 18/20\n",
      "5000/5000 [==============================] - 0s 71us/sample - loss: 0.3055 - val_loss: 0.3049\n",
      "Epoch 19/20\n",
      "5000/5000 [==============================] - 0s 72us/sample - loss: 0.3036 - val_loss: 0.3053\n",
      "Epoch 20/20\n",
      "5000/5000 [==============================] - 0s 73us/sample - loss: 0.3045 - val_loss: 0.3039\n"
     ]
    }
   ],
   "source": [
    "stacked_encoder = tf.keras.models.Sequential([\n",
    "    #     tf.keras.layers.Flatten(input_shape=[28, 28]),\n",
    "    #     tf.keras.layers.Dense(100, activation='selu'),\n",
    "    tf.keras.layers.Dense(100, input_shape=[784], activation='relu'),\n",
    "    tf.keras.layers.Dense(30, activation='selu'),\n",
    "])\n",
    "\n",
    "stacked_decoder = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Dense(100, activation='selu', input_shape=[30]),\n",
    "    tf.keras.layers.Dense(28 * 28, activation='sigmoid'),\n",
    "#     tf.keras.layers.Reshape([28, 28]),\n",
    "])\n",
    "\n",
    "stacked_ae = tf.keras.models.Sequential([stacked_encoder, stacked_decoder])\n",
    "\n",
    "stacked_ae.compile(loss=\"binary_crossentropy\",\n",
    "                   optimizer=tf.keras.optimizers.SGD(lr=1.5))\n",
    "history = stacked_ae.fit(x_train,\n",
    "                         x_train,\n",
    "                         epochs=20,\n",
    "                         validation_data=[x_valid, x_valid])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T09:22:53.113299Z",
     "start_time": "2020-04-27T09:22:52.949049Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x216 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_image(image):\n",
    "    image = image.reshape(28, 28)\n",
    "    plt.imshow(image, cmap='binary')\n",
    "    plt.axis('off')\n",
    "\n",
    "\n",
    "def show_reconstructions(model, n_images=5):\n",
    "    reconstructions = model.predict(x_val[:n_images])\n",
    "    fig = plt.figure(figsize=(n_images * 1.5, 3))\n",
    "    for image_index in range(n_images):\n",
    "        plt.subplot(2, n_images, 1 + image_index)\n",
    "        plot_image(x_val[image_index])\n",
    "        plt.subplot(2, n_images, 1 + n_images + image_index)\n",
    "        plot_image(reconstructions[image_index])\n",
    "\n",
    "\n",
    "show_reconstructions(stacked_ae)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 自编码器用于可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `autoencoder`降维然后用于可视化，效果不顾好；但可以方便处理大量具有大量特征的数据；\n",
    "- 因此可以先用其将数据维度降低到一定大小，然后使用其它降维方法处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T09:54:35.142062Z",
     "start_time": "2020-04-27T09:54:28.741947Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff2436e3190>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "\n",
    "# 自编码器减小数据维度到30维\n",
    "x_valid_compressed = stacked_encoder.predict(x_valid)\n",
    "\n",
    "# tsne将数据降低到二维\n",
    "tsne = TSNE()\n",
    "x_valid_2d = tsne.fit_transform(x_valid_compressed)\n",
    "\n",
    "plt.scatter(\n",
    "    x_valid_2d[:, 0],\n",
    "    x_valid_2d[:, 1],\n",
    "    c=y_valid,\n",
    "    s=10,\n",
    "    cmap='tab10',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T09:57:29.099554Z",
     "start_time": "2020-04-27T09:57:18.523437Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "\n",
    "# adapted from https://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html\n",
    "plt.figure(figsize=(10, 8))\n",
    "cmap = plt.cm.tab10\n",
    "plt.scatter(x_valid_2d[:, 0], x_valid_2d[:, 1], c=y_valid, s=10, cmap=cmap)\n",
    "image_positions = np.array([[1., 1.]])\n",
    "for index, position in enumerate(x_valid_2d):\n",
    "    dist = np.sum((position - image_positions)**2, axis=1)\n",
    "    if np.min(dist) > 0.02:  # if far enough from other images\n",
    "        image_positions = np.r_[image_positions, [position]]\n",
    "        imagebox = mpl.offsetbox.AnnotationBbox(\n",
    "            mpl.offsetbox.OffsetImage(\n",
    "                x_valid[index].reshape(28, 28),\n",
    "                cmap=\"binary\",\n",
    "            ),\n",
    "            position,\n",
    "            bboxprops={\n",
    "                \"edgecolor\": cmap(y_valid[index]),\n",
    "                \"lw\": 2,\n",
    "            },\n",
    "        )\n",
    "        plt.gca().add_artist(imagebox)\n",
    "plt.axis(\"off\")\n",
    "# save_fig(\"fashion_mnist_visualization_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 无监督预训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大量数据，但只有少量是有标签的；首先使用所有数据训练一个`autoencoder`模型，然后冻结并复用其低层网络，创建新的网络，在有标签的数据上继续训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绑定权重\n",
    "当`autoencoder`对称时，一个常用的技巧是将解码层的权重和编码层的权重绑定起来，将模型参数减半，加速训练降低过拟合风险"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DenseTranspose(tf.keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    类似密集层，但使用其它密集层的权重，和自己的偏置\n",
    "    \"\"\"\n",
    "    def __init__(self, dense, activation=None, **kwargs):\n",
    "        self.dense = dense\n",
    "        self.activation = tf.keras.activations.get(activation)\n",
    "        super().__init__(**kwargs)\n",
    "\n",
    "    def build(self, batch_input_shape):\n",
    "        self.biases = self.add_weight(name='bias',\n",
    "                                      initializer='zeros',\n",
    "                                      shape=[self.dense.input_shape[-1]])\n",
    "        super().build(batch_input_shape)\n",
    "\n",
    "    def call(self, inputs):\n",
    "        z = tf.matmul(inputs, self.dense.weights[0], transpose_b=True)\n",
    "        return self.activation(z + self.biases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dense_1 = keras.layers.Dense(100, activation=\"selu\")\n",
    "dense_2 = keras.layers.Dense(30, activation=\"selu\")\n",
    "tied_encoder = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28]),\n",
    "    dense_1,\n",
    "    dense_2,\n",
    "])\n",
    "tied_decoder = keras.models.Sequential([\n",
    "    DenseTranspose(dense_2, activation=\"selu\"),\n",
    "    DenseTranspose(dense_1, activation=\"sigmoid\"),\n",
    "    keras.layers.Reshape([28, 28]),\n",
    "])\n",
    "tied_ae = keras.models.Sequential([tied_encoder, tied_decoder])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Convolutional Autoencoders`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_encoder = keras.models.Sequential([\n",
    "    keras.layers.Reshape([28, 28, 1], input_shape=[28, 28]),\n",
    "    keras.layers.Conv2D(16, kernel_size=3, padding=\"same\", activation=\"selu\"),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    keras.layers.Conv2D(32, kernel_size=3, padding=\"same\", activation=\"selu\"),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    keras.layers.Conv2D(64, kernel_size=3, padding=\"same\", activation=\"selu\"),\n",
    "    keras.layers.MaxPool2D(pool_size=2)\n",
    "])\n",
    "conv_decoder = keras.models.Sequential([\n",
    "    keras.layers.Conv2DTranspose(32,\n",
    "                                 kernel_size=3,\n",
    "                                 strides=2,\n",
    "                                 padding=\"valid\",\n",
    "                                 activation=\"selu\",\n",
    "                                 input_shape=[3, 3, 64]),\n",
    "    keras.layers.Conv2DTranspose(16,\n",
    "                                 kernel_size=3,\n",
    "                                 strides=2,\n",
    "                                 padding=\"same\",\n",
    "                                 activation=\"selu\"),\n",
    "    keras.layers.Conv2DTranspose(1,\n",
    "                                 kernel_size=3,\n",
    "                                 strides=2,\n",
    "                                 padding=\"same\",\n",
    "                                 activation=\"sigmoid\"),\n",
    "    keras.layers.Reshape([28, 28])\n",
    "])\n",
    "conv_ae = keras.models.Sequential([conv_encoder, conv_decoder])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Recurrent Autoencoders`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "recurrent_encoder = keras.models.Sequential([\n",
    "    keras.layers.LSTM(100, return_sequences=True, input_shape=[None, 28]),\n",
    "    keras.layers.LSTM(30)\n",
    "])\n",
    "recurrent_decoder = keras.models.Sequential([\n",
    "    keras.layers.RepeatVector(28, input_shape=[30]),\n",
    "    keras.layers.LSTM(100, return_sequences=True),\n",
    "    keras.layers.TimeDistributed(keras.layers.Dense(28, activation=\"sigmoid\"))\n",
    "])\n",
    "recurrent_ae = keras.models.Sequential([recurrent_encoder, recurrent_decoder])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Denoising Autoencoders`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropout_encoder = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28]),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(100, activation=\"selu\"),\n",
    "    keras.layers.Dense(30, activation=\"selu\")\n",
    "])\n",
    "dropout_decoder = keras.models.Sequential([\n",
    "    keras.layers.Dense(100, activation=\"selu\", input_shape=[30]),\n",
    "    keras.layers.Dense(28 * 28, activation=\"sigmoid\"),\n",
    "    keras.layers.Reshape([28, 28])\n",
    "])\n",
    "dropout_ae = keras.models.Sequential([dropout_encoder, dropout_decoder])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Sparse Autoencoders`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparse_l1_encoder = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[28, 28]),\n",
    "    keras.layers.Dense(100, activation=\"selu\"),\n",
    "    keras.layers.Dense(300, activation=\"sigmoid\"),\n",
    "    keras.layers.ActivityRegularization(l1=1e-3)\n",
    "])\n",
    "sparse_l1_decoder = keras.models.Sequential([\n",
    "    keras.layers.Dense(100, activation=\"selu\", input_shape=[300]),\n",
    "    keras.layers.Dense(28 * 28, activation=\"sigmoid\"),\n",
    "    keras.layers.Reshape([28, 28])\n",
    "])\n",
    "sparse_l1_ae = keras.models.Sequential([sparse_l1_encoder, sparse_l1_decoder])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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